VIP内容
R和Python语言是Power BI的强大补充。它们支持高级的数据转换技术，这些技术在Power BI的默认配置中很难执行，但通过利用R和Python的功能变得更容易。如果您是一位业务分析师、数据分析师或数据科学家，希望推动Power BI，并将其从一个商业智能工具转变为一个高级数据分析工具，那么这本书将帮助您实现这一目标。
你将学到什么
- 使用ggplot2包通过R创建高级数据可视化
- 使用R和Python摄取数据，以克服Power查询的一些限制
- 使用R和Python对数据应用机器学习模型，而不需要Power BI premium compacity
- 通过微软认知服务、IBM Watson自然语言理解和SQL Server机器学习服务中的预训练模型，在Power - - BI中加入高级人工智能，而不需要Power BI premium compacity
- 使用R和Python执行Power BI中不可能执行的高级字符串操作
这本书是给谁的
- 普通用户、数据分析师和数据科学家
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Advanced Analytics in Power BI with R and Python Ingesting, Transforming, Visualizing
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最新内容
Advances in natural language processing have resulted in increased capabilities with respect to multiple tasks. One of the possible causes of the observed performance gains is the introduction of increasingly sophisticated text representations. While many of the new word embedding techniques can be shown to capture particular notions of sentiment or associative structures, we explore the ability of two different word embeddings to uncover or capture the notion of logical shape in text. To this end we present a novel framework that we call Topological Word Embeddings which leverages mathematical techniques in dynamical system analysis and data driven shape extraction (i.e. topological data analysis). In this preliminary work we show that using a topological delay embedding we are able to capture and extract a different, shape-based notion of logic aimed at answering the question "Can we find a circle in a circular argument?"
最新论文
Advances in natural language processing have resulted in increased capabilities with respect to multiple tasks. One of the possible causes of the observed performance gains is the introduction of increasingly sophisticated text representations. While many of the new word embedding techniques can be shown to capture particular notions of sentiment or associative structures, we explore the ability of two different word embeddings to uncover or capture the notion of logical shape in text. To this end we present a novel framework that we call Topological Word Embeddings which leverages mathematical techniques in dynamical system analysis and data driven shape extraction (i.e. topological data analysis). In this preliminary work we show that using a topological delay embedding we are able to capture and extract a different, shape-based notion of logic aimed at answering the question "Can we find a circle in a circular argument?"
